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一种基于深度学习的全自动方法,用于细分感兴趣的区域,并预测儿科动态脏扫描中的功能

Xueli Ji¹, Guohui Zhu², Jinyu Gou¹

¹Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.

Annals of nuclear medicine

|February 20, 2024

概括

一个深度学习模型准确地对儿科扫描中脏区域进行细分,从而能够精确地评估功能. 这种自动化方法改善了动态脏扫描 (DRS) 的分析,以更好地照顾患者.

科学领域:

核医学是一种核医学.
医疗保健中的人工智能
儿科成像学儿科成像学

背景情况:

准确的部区域划分对于儿科动态部扫描 (DRS) 是至关重要的.
手动细分是耗时的,容易引起观察者之间的变化.
深度学习为自动化和准确的图像分析提供了潜力.

研究的目的:

开发和评估深度学习 (DL) 模型,用于完全自动的脏ROI细分.
评估DL模型在儿科^99mTechnetium-ethylenedicysteine (^99mTc-EC) DRS中计算功能参数的能力.

主要方法:

对1283个儿科DRS数据集进行了回顾性分析.
开发和评估一个完全自动的ROI细分 (FASR) 深度学习模型.
使用IOU,DSC,ICC和皮尔森相关性对血液 perfusion rate (BPR) 和功能差异 (DRF) 的自动与手动细分进行比较.

主要成果:

该FASR模型表现出高性能,精度为0.88,召回为0.94,IOU为0.83和DSC为0.91.
对于BPR (r=0.94) 和DRF (r=0.97) 的自动化方法和手动方法之间有很好的相关性.
对于BPR和DRF来说,0.94的高类内相关系数 (ICC) 表明了强烈的共识.

结论:

开发了一种可靠和稳定的深度学习模型,用于在儿科DRS中自动化脏ROI细分.
DL模型准确预测功能参数,为临床实践提供了有价值的工具.
这种自动化方法提高了儿科功能评估的效率和一致性.

关键词:

深度学习是一种深度学习.动态脏光学扫描 (Dynamic renal scintigraphy) 是一种动态脏光学扫描.儿科儿科儿科感兴趣的地区功能功能功能

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